System and Method for Image Analysis of Multi-Dimensional Data

ABSTRACT

A computer-implemented system and method for analyzing a biological sample are disclosed. A sequence of images of the biological sample from an image capture device are received from an image capture device, wherein each image of the sequence of images is acquired at a particular focal plane of the biological sample. In addition, an object map is developed from the sequence of images and the object map is analyzed to measure a three-dimensional characteristic of a particular object. The object map comprises a plurality of object map planes, each object map plane is associated with one image of the sequence of images, and pixels of the object map planes associated with the particular object in the sequence of images are assigned a unique identifier associated with the particular object.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of Cohen et al., U.S. patentapplication Ser. No. 15/897,699, filed on Feb. 15, 2018, and entitled“System and Method for Image Analysis of Multi-Dimensional Data”(Attorney Docket No. MD-0117USCON); which in turn is a continuation ofCohen, et al., U.S. application Ser. No. 15/019,411, filed Feb. 9, 2016,and entitled “System and Method for Image Analysis of Multi-DimensionalData” (Attorney Docket No. MDV-0117US) and which issued as U.S. Pat. No.9,928,403. The entire contents of these applications are incorporatedherein by reference.

FIELD OF DISCLOSURE

The present subject matter relates to high-content imaging systems andmore particularly, to a system to analyze multi-dimensional datagenerated using such systems.

BACKGROUND

A high-content imaging system (HCIS) may be used to obtain a microscopyimage of a biological sample. Such image may include a number of cellsagainst a background field. Further, the HCIS may be used to obtain aseries of microscopy images of the biological sample, wherein, forexample, each image is obtained using a different focus point. Suchseries of microscopy images may be combined to develop athree-dimensional view of the biological sample. Such series ofmicroscopy images may also be analyzed to segment and identify a portionof each such image that is associated with a particular cell. Suchportions may then be combined to form a three-dimensional view of theparticular cell, analyzed further to identify organelles within thethree-dimensional cell body, and/or develop three-dimensional statisticsof the three-dimensional cell body and/or the organelles therein.

A researcher may want to obtain statistics of cells that are present inthe microscopy image or series of microscopy images. Such statistics mayinclude a count of how may cells of a particular cell type are presentin the image, the range of sizes (e.g., dimensions, volumes and surfaceareas) of such cells, the mean, median and mode of the sizes of suchcells, how well the cell conforms to particular shape (e.g.,sphericity), and the like. Further, the images may be analyzed toidentify organelles within cells identified in such images and thestatistics of such organelles may also be developed. Before any suchstatistics can be calculated, cells in the microscopy image must besegmented from the background and also from any debris present in themicroscopy image.

An interactive image analysis system may be used to specify a sequenceof image analysis steps and parameters for each step. Examples of imageanalysis steps include, for example, select an image captured using aparticular illumination source, threshold the image, and apply one ormore filters to the image. Example filters may include unsharp mask, asmoothing filter, and the like. After the sequence of image analysissteps and corresponding parameters are specified, measurements may betaken of the image that results when the specified the image analysissteps are applied. Such measurements include a count of different typesof objects (e.g., cells or components of cells) that are present in theresulting image. Cohen et al., U.S. Pat. No. 8,577,079, entitled “IMAGEPROCESSING SYSTEM PROVIDING SELECTIVE ARRANGEMENT AND CONFIGURATION FORAN IMAGE ANALYSIS SEQUENCE,” describes one such computer-implementedsystem for processing a selected image or series of images usingmultiple processing operations. The entire contents of this patent areincorporated herein by reference.

SUMMARY

According to one aspect, a computer-implemented system for analyzing abiological sample includes one or more processors and executable codestored in one or more non-transitive storage devices. The executablecode, when executed, causes the one or more processors to receive asequence of images of the biological sample from an image capturedevice, wherein each image of the sequence of images is acquired at aparticular focal plane of the biological sample. In addition, executionof the code causes the one or more processors to develop an object mapfrom the sequence of images and analyze the object map to measure athree-dimensional characteristic of the particular object. The objectmap comprises a plurality of object map planes, each object map plane isassociated with one image of the sequence of images, and pixels of theobject map planes associated with a particular object in the sequence ofimages are assigned a unique identifier associated with the particularobject.

According to another aspect, a method for analyzing a biological sampleincludes receiving from an image capture device a first sample image anda second sample image, wherein the first and second sample images areimages of the biological sample acquired at first and second focalplanes, respectively, thereof. The method also includes analyzing thefirst sample image to identify first connected pixels of the firstsample image, wherein the first connected pixels are associated with anobject, and analyzing the second sample image to identify secondconnected pixels of the second sample image that at least partiallyoverlap the first connected pixels, wherein the second connected pixelsare associated with the object. Further, the method includes associatingthird connected pixels of a first object map plane with an identifier,associating fourth connected pixels of a second object map plane withthe identifier, and measuring a three-dimensional characteristic of theobject in accordance with the first and second object map planes. Thefirst object map plane is associated with the first sample image, thethird connected pixels correspond to the first connected pixels, and theidentifier is associated with the object. Further, the second object mapplane is associated with the second sample image and the fourthconnected pixels correspond to the second connected pixels.

Other aspects and advantages will become apparent upon consideration ofthe following detailed description and the attached drawings whereinlike numerals designate like structures throughout the specification.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a high-content imaging system in accordancewith the present disclosure;

FIG. 2 is a block diagram of an image analysis system for analyzingmulti-dimensional images captured using the high-content imaging systemof FIG. 1;

FIGS. 3A-3C are screens displayed by the image analysis system of FIG.2;

FIG. 4 is a flowchart of processing undertaken by an image analysismodule of the image analysis system of FIG. 2;

FIG. 5 is a block diagram of an embodiment of an image analysis moduleof the image analysis system of FIG. 2;

FIG. 6A is a portion of a screen displayed by the image analysis systemof FIG. 2 that allows specification of an image analysis step;

FIG. 6B is an example image generated by the image analysis module inaccordance with the image analysis step specified by the screen of FIG.6A; and

FIG. 6C is an example table of measurements generated by the imageanalysis module in accordance with the image analysis step specified bythe screen of FIG. 6A.

DETAILED DESCRIPTION

Referring to FIG. 1, as will be apparent to those who have skill in theart, an HCIS 100 may include an X-Y stage 102, one or more objectivelenses 104, one or more illumination sources 106, one or more filters108, an image capture device 110, and a controller 112. The HCIS 100 mayalso include one or more mirrors 114 that direct light from theillumination source 106 to a sample tray 116 that may be disposed on theX-Y stage 102, and from such sample tray 116 to the image capture device110. Typically, the sample tray 116 includes a plurality of wells 118,and samples (for example, biological cells) to be imaged by the HCIS 100may be disposed in each such well 118.

Although, FIG. 1 shows the light from the illumination source 106reflected from sample tray 116 reaching the image capture device 110, itshould be apparent that additional mirrors (not shown) may be used sothat light from the illumination source 106 is transmitted through thesample tray 116 and directed toward the image capture device 110.Further, it should be apparent that in some cases no illumination fromthe illumination source 106 may be necessary to image the samples in thesample tray 116 (for example, if the samples emit light or if thesamples include radioactive components). In some embodiments, light fromthe illumination source may be transmitted through the samples in thesample tray 116, and the samples refract and/or absorb the transmittedlight to produce light that is imaged.

During operation, the sample tray 116 may be placed, either manually orrobotically, on the X-Y stage 102. In addition, the controller 112 mayconfigure the HCIS 100 to use a combination of a particular objectivelens 104, illumination generated by the illumination source 106, and/orfilter 108. For example, the controller 112 may operate positioningdevices (not shown) to place a selected objective lens 104 and,optionally, a selected filter 108 in the light path between the sampletray 116 and the image capture device 110. The controller 112 may alsodirect the illumination source 106 to illuminate the sample tray 116with particular wavelengths of light. The samples in the sample tray 116may contain molecules that fluoresce, either naturally occurringmolecules, or molecules produced or present within the samples due totreatment. The wavelength illuminating the sample may be the excitationwavelengths associated with such fluorescent molecules, and the imagingcapture device will capture only the emission spectrum of suchfluorescent materials. One or more wavelengths may used serially orsimultaneously to illuminate the same samples and produce images.

To obtain a series of images at different focal positions, thecontroller 112 operates a focus mechanism 120 so that the image capturedevice 110 may obtain in-focus images of the sample disposed in thesample tray 116 at each such focal position.

Referring to FIG. 2, a multi-dimensional image analysis system 200 forspecifying a sequence of image analysis steps for processingtwo-dimensional and three-dimensional images from an HCIS 100 includesan image acquisition module 202 in communications with the image capturedevice 112 of the HCIS 100.

The image acquisition module 202 directs the controller 112 to capture aseries of images of a biological sample at various focal positions. Inone embodiment, the image acquisition module 202 may direct thecontroller 112 to operate the focus mechanism 120 and the image capturedevice 112 to capture a series of between 10 and 150 successive imagesof the sample. To obtain a low-resolution three-dimensionalrepresentation of the sample, the image acquisition module 202 maydirect the controller 112 to adjust the focal position by approximately50 microns between successive images. To obtain a high-resolutionthree-dimensional representation of the sample, the image acquisitionmodule 202 may direct the controller 112 to adjust the focal position bybetween approximately 3 microns and 5 microns between successive images.For an even, higher-resolution three-dimensional representation of thesample, the image acquisition module 202 may direct the controller 112to adjust the focal position by between 0.5 microns and 1 micron betweensuccessive images.

Further, the image acquisition module 202 may direct the controller tocapture multiple series of images, wherein each series of images iscaptured using a different combination of an illumination source 106,filter 108, and objective lens 104.

The image acquisition module 202 receives each series of successivesource images of the sample from the image capture device 112 and storessuch images in a source images data store 204. Each series of sourceimages may be considered a three-dimensional representation of thevolume that includes the biological sample, and each source image of theseries may be considered a two-dimensional plane (or slice) of thethree-dimensional volume. The biological sample may include one or morecells. Larger cells may be represented in all of the planes of thethree-dimensional representation, and smaller cells may be representedin only some planes of the three-dimensional representation.

After the series of source images are stored in the source images datastore 204, a user interface module 206 allows an operator to use a usercomputer 208 to select one or more of the series of source images. Theselected series is displayed on the screen of the user computer 208. Theuser may specify to the user interface module 206, a sequence ofthree-dimensional and two-dimensional image analysis steps to perform onthe displayed series of images. An image or series of images that isdeveloped after the sequence of image analysis steps have beenundertaken is displayed on the screen of the user computer 208.

Referring to FIGS. 3A-3C, the user interface module 206 displays on thescreen 300 of the user computer 208 a region 302 in which panels forspecifying image analysis steps may be displayed, a region 304 in whichone or more images may be displayed, and a menu 306 of available imageanalysis steps.

Referring to FIG. 3B, selecting a menu item in the menu 306 displays acorresponding panel 308. Each panel is associated with a particularimage analysis step. The panel includes a pop-up menu 310 from which aninput (or source) plane or a series of images may be selected. The panelalso includes a text box 312 in which the user may enter an identifierassociated with an output (or result) plane or series of images thatresult from applying the particular image analysis step to the selectedinput plane or series of images.

The pop-up menu 310 presents identifiers associated with each series ofimages captured by the image capture device using different imagingparameters or the identifier associated with the output of another panel308.

Each panel 308 also includes one or more text-boxes and/or selectionboxes 314 to specify parameters associated with the particular imageanalysis step specified using such panel 308. For example, the panel 308a is associated with an image analysis step of “Find Spherical Objects,”and uses as input a series of images associated with an identifier DAPI(310 a). The panel includes text boxes 314 for specifying a minimum andmaximum width of spherical objects, and intensity of such objectscompared to the background, and a minimum and maximum number of planeseach identified object may span. The result of applying the imageanalysis step specified by the panel 308 a is associated with an outputseries of images named “Find Spherical Objects” (312 a).

Further, the panel 308 b is associated with an image analysis step of“Count Nuclei Objects” also in the series of images associated with theidentifier DAPI (310 b). The result of the panel 308 b is associatedwith the identifier “Count Nuclei Objects” (312 b).

The panel 308 c uses as an input an image associated with the identifier“Count Nuclei Objects” (310 c), which is the output of the imageanalysis step specified by the panel 308 b, and generates an outputseries of images associated with an identifier “Nuclei” (312 c).

Because the inputs of the panel 308 a and 308 b do not depend on oneanother, the system 200 may undertake the image analysis stepsidentified by such panels concurrently. The system 200 undertakes theimage analysis steps identified by the panel 308 c only after the outputof the panel 308 b is available.

Further, for example, the panels 308 a and 308 c specifythree-dimensional image analysis steps that require analysis acrossimage planes because these image analysis step analyze information frommultiple image planes in concert to produce a result. In contrast, thepanel 308 b specify a two-dimensional image analysis steps that analyzeseach plane of the series separately.

For example, the count nuclei image analysis step specified by panel 308b analyzes the pixels in an image plane to separate pixels that areassociated with an object from pixels that are part of the background.The image may be processed (for example, smoothed, thresholded, andanalyzed) to associate connected pixels that are too small with thebackground and break up connected pixels that specify one object that istoo large into separate objects. Each image plane may be processedindependently in accordance with the image analysis step specified bypanel 308 b.

In contrast, the connect by best match image analysis step specified bypanel 308 c analyzes multiple planes to identify object pixels in suchplanes that are all associated with the same object in three dimensions.In particular, the connect by best match image analysis step identifiespixels that are connected across image planes and associates suchidentified pixel with the same object. In one embodiment, the connect bybest match image analysis step associates an object-id with each objectin an image plane identified by the count nuclei image analysis step. Anobject map image plane is created from each image plane in the series ofimages, and the value each pixel of the object map image plane thatcorresponds to an object pixel in the image plane is set to object-id ofthe object.

Thereafter, pixels associated with an object in each object map imageplane are compared to pixels associated with objects in other object mapimage planes to determine if such pixels of these different planes areassociated with the same object (i.e., connected across planes). If so,these pixels of the different planes are assigned the same object id.The result of such analysis is an object map series of images thatidentifies individual objects in the captured series of image in threedimensions. Such object map series of images may be analyzed to measurethree-dimensional characteristics (such as sphericity, shape, volume,and the like) of the objects of the captured series of images.

First connected pixels in a first object map image plane and secondconnected pixels in a second object map image plane may be associatedwith the same object (and assigned the same object-id) if the first andsecond connected pixels at least partially overlap. Additionally, thefirst and the second connect pixels may be considered to be associatedwith the same object if such pixels are within a pre-determined distance(in three-dimensions) from one another and if the differences inintensities of the corresponding pixels in the series of images arewithin a predetermined threshold.

Further, the object map series of images may be analyzed to make surethat connected pixels in each object map plane is associated with atmost one object. Further, if the first pixels of the first object mapplane and second pixels of the second object plane are associated with aparticular object (i.e., have the same object id) and if there are morethan a predetermined number of intervening planes between the first andthe second object map planes that do not have any pixels associated withthe particular object, the first pixels and second pixels will beassociated with different objects (i.e., given different object-ids). Asshould be apparent, the image analysis step specified by the panel 308 crequires analysis of multiple image planes, and each image plane may notbe processed independently from other image planes.

Referring to FIG. 3C, the user-interface displays in the region 304 theresults of the image analysis steps specified in the region 300.Applying the series of image analysis steps results in one or more imageprocessed series of images, and one plane of such image-processed seriesof images is displayed in one or more corresponding regions 350. In someembodiments, the user may hide the results of particular imageprocessing steps, determine if only one image plane is displayed, orspecify display of a composite image generated from the series of imageplanes.

A slider 352 is also displayed, for example, adjacent to one of theimages displayed in the regions 304. Adjusting the slider 352 by oneunit upward displays subsequent plane from the series of source imagesin the region 304. If multiple planes, each from a different series ofimages, are displayed in the region 304, adjusting the slider 352selects a subsequent or preceding plane of each such series to display.Similarly, adjusting the slider 306 downward displays preceding planesin the panels 302 and 304.

A threshold slider 354 may be displayed adjacent to some imagesdisplayed in the region 300, and adjusting such slider 354 dynamicallyadjusts the brightness of the displayed image based on intensity.

Each panel 308 includes a button 316 and selecting such buttonimmediately undertakes the image-processing step specified by suchpanel. If such image-processing step requires an input that is an outputof another panel to be generated, the image processing system 200generates such output. The image or series of images that results fromsuch image-processing step is displayed immediately in the region 304 ofthe screen 300 of the user computer 208. In this manner, the user mayinteractively adjust parameters and select the order in which multipleimage-processing steps are undertaken in two-dimensions andthree-dimensions to analyze the biological sample.

Referring once again to FIG. 3B, the image analysis steps defined in theregion 300 may be associated with an identifier using the text box 318and saved. All of the image analysis steps defined in the 300 mayexecuted by selecting a button 320. Further, such image analysis stepsmay be executed without update to the images displayed in the region 304(i.e., in a batch mode) to process one or more series of images capturedusing the HCIS 100.

Referring to FIGS. 2 and 3B, when the user presses the button 316 or thebutton 320 the user interface module 206 provides the specified imageanalysis steps and the user supplied parameters associated with suchsteps to the image analysis module 208.

FIG. 4 illustrates a flowchart 400 of the steps undertaken by the imageanalysis module 208. At step 402, the image analysis module 208 selectsone of the steps specified by the user. At step 404, the image analysismodule 208 determines if the input image or series of images isavailable (i.e., has been created). If so, the image analysis module 208proceeds to step 406. Otherwise, at step 408, the image analysis module208 undertakes the image analysis step necessary to develop the input,and then proceeds to step 406.

At step 406, the image analysis module 208 processes the input inaccordance with the parameters associated with the image analysis stepselected at step 402. At step 410, the image analysis module 208 storesthe results of undertaking the image analysis step. At step 412, theimage analysis module 208 determines if there are any image analysissteps specified by the user that have not been undertaken, and if so,proceeds to step 402. Otherwise the image analysis module 208 notifiesthe user interface module that the output image or series of images areready, at step 414, and exits.

Referring also to FIG. 5, some image processing steps may beparallelizable. In such cases, at step 208, a distribution processor 420of the image processing module 208 may supply the image processingfunction associated with the image processing step and the parameters ofthe image processing step to each one of a plurality of processors 422.In addition, the distribution processor 420 may assign a unique portionof the input (either a plane of a series of images or a portion of aplane) to each one of a plurality of processors 422. The plurality ofprocessors 422 undertake the image analysis step on the unique portionof the input assigned thereto and provide the result of such imageanalysis step to an output combination processor 424. The outputcombination processor 424 combines the output generated by eachprocessor 422 to create the output of the image analysis step.

The image analysis system 200 may allow the user to specify multipleinput sources to use in an image analysis step. Referring to FIGS. 6Aand 6B, a panel 450 associated with a measure volume image analysis stepthat includes three pull down menus 452 a, 452 b, and 452 c. The pulldown menu 452 a specifies a first input to this image analysis step tobe the output of the image analysis step specified using the panel 308 a(FIG. 3B), the pull down menu 452 b species a second input to be DAPI,and the pull down menu 452 c specifies a third input to be FITC. In thisexample, DAPI and FITC are different imaging conditions under whichseparate series of images of the biological sample were captured. TheDAPI series of images may be used to identify cell or spheroids ofcells. The FITC series of image may be used to identify nuclei withincells.

The image analysis step specified in the panel 450 applies the inputspecified using the pull down menu 452 a as a mask to the inputsspecified using the pull down menu 450 b and 450 c to identify bothobject and nuclei in such objects. In this example, each plane of theseries “Find Spherical Objects” selected using the pull-down menu 452 ais masked with both a corresponding plane of the series “DAPI” selectedusing the pull-down menu 452 b and a corresponding plane of the series“FITC” selected using the pull-down menu 452 c. The corresponding planesof the two series of images that result from the two masking operationsare merged into a single plane of an output of series of images thatresults from this step.

FIG. 6B shows an example of an image 460 that may be generated anddisplayed in the region 304 of the display of the user computer 208after undertaking the image analysis step specified using the panel 450.As shown in the image, objects 462 and sub-objects 464 within suchobjects 462 have been identified.

The panel 450 also generates a table shown in FIG. 6C of the volumes ofthe objects identified in the FIG. 6B. Such table may also be displayedon the display of the user computer 208 or downloaded thereto.

Examples of three-dimensional image processing steps that may bespecified using the panels described above include apply Mean, Gaussian,and similar filters in three-dimensions; convolve a three-dimensionalkernel with a series of images that represent an three-dimensionalvolume; apply a two-dimensional mask to each plane of a series of imagesthat represent a three-dimensional volume; generate a three-dimensionalobject map wherein a unique pixel intensity in each plane of a series ofoutput images is associated with a unique object in a series of images;identify pixels of a first object and a second object in correspondingfirst and second adjacent planes that overlap or are adjacent acrossplanes, and associate the pixels of the first object and the secondobject with a same unique identifier; crop a volume represented in aseries of images to reduce the size of the input for further imageanalysis steps; connect objects between adjacent planes; and the like.

Examples of measurements that may be taken using three-dimensional imageprocessing steps that may be specified using the panels described aboveinclude measure volumes of three-dimensional objects, classify shapes ofthree-dimensional objects, measure a distance in three-dimensionsbetween objects, measure distance in three-dimensions between a featureof an object and a center of such object, and the like.

It should be apparent, that the series of images described herein abovemay be a series of images taken over time, and time instead of volumeanalysis may be undertake using the image analysis system 200. Suchanalysis may be used to automatically track the position and velocity ofan object in the field of view of the image capture device 110. Further,characteristics such as size, intensity, shape and similar features asthe object moves in the field of view and changes in suchcharacteristics may also be measured using the image analysis system 200described above.

It should be apparent to those who have skill in the art that anycombination of hardware and/or software may be used to implement theimage analysis system described herein. It will be understood andappreciated that one or more of the processes, sub-processes, andprocess steps described in connection with FIGS. 1-6C may be performedby hardware, software, or a combination of hardware and software on oneor more electronic or digitally-controlled devices. The software mayreside in a software memory (not shown) in a suitable electronicprocessing component or system such as, for example, one or more of thefunctional systems, controllers, devices, components, modules, orsub-modules schematically depicted in FIGS. 1-6C. The software memorymay include an ordered listing of executable instructions forimplementing logical functions (that is, “logic” that may be implementedin digital form such as digital circuitry or source code, or in analogform such as analog source such as an analog electrical, sound, or videosignal). The instructions may be executed within a processing module orcontroller (e.g., the image acquisition module 202, the user interfacemodule 206, and the image analysis module 208 of FIG. 2), whichincludes, for example, one or more microprocessors, general purposeprocessors, combinations of processors, digital signal processors(DSPs), field programmable gate arrays (FPGAs), or application-specificintegrated circuits (ASICs). Further, the schematic diagrams describe alogical division of functions having physical (hardware and/or software)implementations that are not limited by architecture or the physicallayout of the functions. The example systems described in thisapplication may be implemented in a variety of configurations andoperate as hardware/software components in a single hardware/softwareunit, or in separate hardware/software units.

The executable instructions may be implemented as a computer programproduct having instructions stored therein which, when executed by aprocessing module of an electronic system, direct the electronic systemto carry out the instructions. The computer program product may beselectively embodied in any non-transitory computer-readable storagemedium for use by or in connection with an instruction execution system,apparatus, or device, such as a electronic computer-based system,processor-containing system, or other system that may selectively fetchthe instructions from the instruction execution system, apparatus, ordevice and execute the instructions. In the context of this document,computer-readable storage medium is any non-transitory means that maystore the program for use by or in connection with the instructionexecution system, apparatus, or device. The non-transitorycomputer-readable storage medium may selectively be, for example, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device. A non-exhaustive list ofmore specific examples of non-transitory computer readable mediainclude: an electrical connection having one or more wires (electronic);a portable computer diskette (magnetic); a random access, i.e.,volatile, memory (electronic); a read-only memory (electronic); anerasable programmable read only memory such as, for example, Flashmemory (electronic); a compact disc memory such as, for example, CD-ROM,CD-R, CD-RW (optical); and digital versatile disc memory, i.e., DVD(optical).

It will also be understood that receiving and transmitting of signals ordata as used in this document means that two or more systems, devices,components, modules, or sub-modules are capable of communicating witheach other via signals that travel over some type of signal path. Thesignals may be communication, power, data, or energy signals, which maycommunicate information, power, or energy from a first system, device,component, module, or sub-module to a second system, device, component,module, or sub-module along a signal path between the first and secondsystem, device, component, module, or sub-module. The signal paths mayinclude physical, electrical, magnetic, electromagnetic,electrochemical, optical, wired, or wireless connections. The signalpaths may also include additional systems, devices, components, modules,or sub-modules between the first and second system, device, component,module, or sub-module.

INDUSTRIAL APPLICABILITY

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

The use of the terms “a” and “an” and “the” and similar references inthe context of describing the invention (especially in the context ofthe following claims) are to be construed to cover both the singular andthe plural, unless otherwise indicated herein or clearly contradicted bycontext. Recitation of ranges of values herein are merely intended toserve as a shorthand method of referring individually to each separatevalue falling within the range, unless otherwise indicated herein, andeach separate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided herein, isintended merely to better illuminate the disclosure and does not pose alimitation on the scope of the disclosure unless otherwise claimed. Nolanguage in the specification should be construed as indicating anynon-claimed element as essential to the practice of the disclosure.

Numerous modifications to the present disclosure will be apparent tothose skilled in the art in view of the foregoing description. It shouldbe understood that the illustrated embodiments are exemplary only, andshould not be taken as limiting the scope of the disclosure.

We claim:
 1. A computer-implemented system for analyzing a biologicalsample includes one or more processors and executable code stored in oneor more non-transitive storage devices, the executable code, whenexecuted, causes the one or more processors to: receive a sequence ofimages of the biological sample from an image capture device, whereineach image of the sequence of images is acquired at a particular focalplane of the biological sample; develop an object map from the sequenceof images, wherein the object map comprises a plurality of object mapplanes, each object map plane is associated with one image of thesequence of images, and pixels of the object map planes associated witha particular object in the sequence of images are assigned a uniqueidentifier associated with the particular object; and analyze the objectmap to measure a three-dimensional characteristic of the particularobject.
 2. The computer-implemented system of claim 1, wherein theexecutable code causes the one or more processors to analyze firstconnected pixels and second connected pixels in first and second objectmap planes of the plurality of object map planes and assign the uniqueidentifier to both the first connected pixels and the second connectedpixels if the first connected pixels and the second connected pixels atleast partially overlap.
 3. The computer-implemented system of claim 2,wherein the executable code causes the one or more processors toidentify first connected pixels associated with object pixels in a firstimage of the sequence of images and second connected pixels associatedwith object pixels in a second image of the sequence of images inparallel.
 4. The computer-implemented system of claim 3, wherein an areaoccupied by the identified first connected pixels in the first image isless than a predetermined amount and the executable code causes the oneor more processors to associate the identified first connected pixelswith a background.
 5. The computer-implemented system of claim 3,wherein the particular object comprises a first object of one or moreobjects in the sequence of images and the unique identifier comprises afirst unique identifier, and an area occupied by the identified firstconnected pixels in the first image is larger than a predeterminedamount and the executable code causes the one or more processors toseparate the first connected pixels in the first image into thirdconnected pixels and fourth connected pixels, and associate pixels ofthe object map plane that are associated with the third connected pixelswith the first unique identifier and pixels of the object map plane thatare associated with the fourth connected pixels with a second uniqueidentifier associated with a second object.
 6. The computer-implementedsystem of claim 3, wherein the sequence of images comprises a firstsequence of images acquired using first imaging conditions, and theexecutable code causes the one or more processors to receive a secondsequence of images of the biological sample acquired using secondimaging conditions and analyze the first and the second sequences ofimages to identify the first and second connected pixels.
 7. Thecomputer-implemented system of claim 1, wherein the particular objectcomprises one or more objects in the sequence of images, and theexecutable code causes the one or more processors to identify a firstplurality of pixels in each image of the sequence of images that areassociated with the one or more objects from a second plurality ofpixels associated with a background.
 8. The computer-implemented systemof claim 1, wherein the image capture device is a component of ahigh-content imaging system.
 9. The computer-implemented system of claim1, further including a graphical user interface that receives a requestto perform an image analysis operation and in response the executablecode causes the one or more processors to develop the object map.
 10. Amethod for analyzing a biological sample, comprising: receiving from animage capture device a first sample image and a second sample image,wherein the first and second sample images are images of the biologicalsample acquired at first and second focal planes, respectively, thereof;analyzing the first sample image to identify first connected pixels ofthe first sample image, wherein the first connected pixels areassociated with an object; analyzing the second sample image to identifysecond connected pixels of the second sample image that at leastpartially overlap the first connected pixels, wherein the secondconnected pixels are associated with the object; associating thirdconnected pixels of a first object map plane with an identifier, whereinthe first object map plane is associated with the first sample image,the third connected pixels correspond to the first connected pixels, andthe identifier is associated with the object; associating fourthconnected pixels of a second object map plane with the identifierwherein the second object map plane is associated with the second sampleimage and the fourth connected pixels correspond to the second connectedpixels; and measuring a three-dimensional characteristic of the objectin accordance with the first and second object map planes.
 11. Themethod of claim 10, wherein the object and the identifier comprise afirst object and a first identifier, respectively, and further includingthe steps of: receiving from the image capture device a third sampleimage that is an image of the biological sample acquired at a thirdfocal plane, wherein the second focal plane is intermediate the firstand third focal planes; analyzing the third sample image to identifyfifth connected pixels thereof that are associated with object pixels;and associating sixth connected pixels of a third object map plane withthe first identifier if the fifth connected pixels at least partiallyoverlap the first and the second connected pixels and with a secondidentifier associated with a second object if the fifth connected pixelsdo not at least partially overlap the first and the second connectedpixels; wherein the step of measuring the three-dimensionalcharacteristic includes the step of measuring the three-dimensionalcharacteristic in accordance with the first object map plane, the secondobject map plane, and the third object map plane.
 12. The method ofclaim 10, wherein the three-dimensional characteristic includes one ofsphericity, shape, and volume of the object.
 13. The method of claim 10,further including analyzing the first sample image to identify fifthconnected pixels thereof that are associated with object pixels.
 14. Themethod of claim 13, further including determining the fifth connectedpixels occupy an area less than a predetermined amount and notassociating any connected pixels of the first object plane thatcorrespond to the fifth connected pixels with an object identifier. 15.The method of claim 13, wherein the identifier comprises a firstidentifier, further including determining that the fifth connectedpixels occupy an area greater than a predetermined amount and separatingthe fifth connected pixels into sixth and seventh connected pixels,associating eighth connected pixels of the first object map plane with asecond identifier and ninth connected pixels of the first object mapplane with a third identifier, wherein the second and third identifiersare associated with second and third objects, respectively.
 16. Themethod of claim 10, wherein the steps of analyzing the first sampleimage and analyzing the second sample image are undertaken in parallelon a plurality of processors.
 17. The method of claim 16, wherein thestep of analyzing the first sample image includes analyzing a firstportion of the first sample image and a second portion of the firstsample image in parallel.
 18. The method of claim 10, wherein the firstsample image is acquired using a first imaging condition, and the stepof analyzing the first sample image includes receiving a third sampleimage using a second imaging condition, and analyzing the first andthird sample images.
 19. The method of claim 10, further includingreceiving a specification of an image processing operation and, inresponse, undertaking the steps of analyzing the first sample image,analyzing the second sample image, associating the third connectedpixels, and associating the fourth connected pixels.